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# Import necessary libraries
import numpy as np
import joblib
import pandas as pd
from flask import Flask, request, jsonify
import traceback
import math
# Define the path where the model is saved
model_file_name = "SuperKart_v1_0.joblib"
try:
# Load the trained machine learning model
model = joblib.load(model_file_name)
except FileNotFoundError:
print(f"Error: Model file not found at {model_file_name}")
model = None
except Exception as e:
print(f"Error loading model: {e}")
traceback.print_exc()
model = None
# Initialize the Flask app
app = Flask(__name__)
@app.route('/')
def home():
return "Welcome to the Super Kart Product Sales Price Prediction API!"
# ---------------- single Prediction Endpoint ----------------
@app.route('/v1/salesprice', methods=['POST'])
def predict_sales_price():
if model is None:
return jsonify({"error": "Model not loaded. Cannot make predictions."}), 500
try:
property_data = request.get_json(force=True)
expected_keys = [
'Product_Weight', 'Product_Sugar_Content', 'Product_Allocated_Area',
'Product_Type', 'Product_MRP', 'Store_Size',
'Store_Location_City_Type', 'Store_Type', 'Store_Age'
]
if not all(key in property_data for key in expected_keys):
missing_keys = [key for key in expected_keys if key not in property_data]
return jsonify({"error": f"Missing keys in input data: {missing_keys}"}), 400
sample = {key: property_data.get(key) for key in expected_keys}
input_data = pd.DataFrame([sample])
predicted_sales_price = model.predict(input_data)
predicted_price = round(float(predicted_sales_price[0]), 2)
if math.isinf(predicted_price) or math.isnan(predicted_price):
return jsonify({"error": "Prediction resulted in an invalid value."}), 400
return jsonify({'Predicted Price': predicted_price}), 200
except Exception as e:
print(f"Error during single prediction: {e}")
traceback.print_exc()
return jsonify({"error": "Internal server error", "details": str(e)}), 500
# ---------------- Batch Prediction Endpoint ----------------
@app.route('/v1/salespricebatch', methods=['POST'])
def predict_sales_price_batch():
"""
Expects a CSV file with one product per row.
Returns JSON: a list of dicts with `row_id` and predicted price.
"""
if model is None:
return jsonify({"error": "Model not loaded. Cannot make predictions."}), 500
if 'file' not in request.files:
return jsonify({"error": "No file uploaded"}), 400
try:
file = request.files['file']
input_data = pd.read_csv(file)
expected_columns = [
'Product_Weight', 'Product_Sugar_Content', 'Product_Allocated_Area',
'Product_Type', 'Product_MRP', 'Store_Size',
'Store_Location_City_Type', 'Store_Type', 'Store_Age'
]
missing_columns = [col for col in expected_columns if col not in input_data.columns]
if missing_columns:
return jsonify({"error": f"Missing required columns: {missing_columns}"}), 400
input_data.reset_index(inplace=True)
input_data.rename(columns={'index': 'row_id'}, inplace=True)
predictions = model.predict(input_data[expected_columns])
predicted_prices = [round(float(p), 2) for p in predictions]
results = [
{"row_id": row_id, "Predicted Price": price}
for row_id, price in zip(input_data['row_id'], predicted_prices)
]
return jsonify(results), 200
except Exception as e:
print(f"Error during batch prediction: {e}")
traceback.print_exc()
return jsonify({"error": "Internal server error during batch prediction.", "details": str(e)}), 500
if __name__ == '__main__':
pass
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